Abstract
While the use of autograders for code correctness is widespread, less effort has focused on automating feedback for good programming style: the tasteful use of language features and idioms to produce code that is not only correct, but also concise, elegant, and revealing of design intent. We present a system that can provide real-time actionable code style feedback to students in large introductory computer science classes. We demonstrate that in a randomized controlled trial, 70 % of students using our system achieved the best style solution to a coding problem in less than an hour, while only 13 % of students in the control group achieved the same. Students using our system also showed a statistically-significant greater improvement in code style than students in the control group.
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- 1.
IRB Protocol number: 2015-10-8003.
References
Ericsson, K., Krampe, R., Tesch-Römer, C.: The role of deliberate practice in the acquisition of expert performance. Psychol. Rev. 100(3), 363–406 (1993)
Fast, E., Steffee, D., Wang, L., Brandt, J., Bernstein, M.: Emergent, crowd-scale programming practice in the IDE. In: SIGCHI Conference on Human Factors in Computing Systems. Toronto (2014)
Fitzpatrick, J.: Applying the ABC metric to C, C++, and Java. In: More C++ Gems, pp. 245–264. Cambridge University Press, New York (2000)
Fowler, M., Beck, K., Brant, J., Opdyke, W., Roberts, D.: Refactoring: Improving the Design of Existing Code. Addison-Wesley, Boston (1999)
Fox, A., Patterson, D.: Engineering Software as a Service. Strawberry Canyon LLC, San Francisco (2014)
Fox, A., Patterson, D., Joseph, S., McCulloch, P.: MAGIC: Massive automated grading in the cloud. In: CHANGEE (Facing the challenges of assessing 21st century skills in the newly emerging educational ecosystem) workshop at EC-TEL 2015, Toledo, Spain (2015)
Gamma, E., Helm, R., Johnson, R., Vlissides, J.: Design Patterns: Elements of Reusable Object-Oriented Software. Addison-Wesley, Boston (1994)
Glassman, E., Singh, R., Miller, R.: Feature engineering for clustering student solutions. In: 1st ACM Conference on Learning at Scale, Atlanta (2014)
Guskey, T.R.: Closing achievement gaps: revisiting Benjamin S. Blooms, “Learning for Mastery”. J. Adv. Acad. 19(1), 8–31 (2007)
Huang, J., Piech, C., Nguyen, A., Guibas, L.: Syntactic and functional variability of a million code submissions in a machine learning MOOC. In: International Conference on Artificial Intelligence in Education (AIED), Memphis (2013)
Johnson, S.: Lint, a C program checker. Technical report 65, Bell Labs (1977)
Kan, S.H.: Metrics and Models in Software Quality Engineering, 2nd edn. Addison-Wesley, Boston (2002)
Kulkarni, C.E., Bernstein, M.S., Klemmer, S.R.: PeerStudio: rapid peer feedback emphasizes revision and improves performance. In: 2nd ACM Conference on Learning at Scale. Vancouver (2015)
Lazar, T., Bratko, I.: Data-driven program synthesis for hint generation in programming tutors. In: Trausan-Matu, S., Boyer, K.E., Crosby, M., Panourgia, K. (eds.) ITS 2014. LNCS, vol. 8474, pp. 306–311. Springer, Heidelberg (2014)
Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(2579–2605), 85 (2008)
Martin, R.C.: Clean Code: A Handbook of Agile Software Craftsmanship. Prentice Hall, Upper Saddle River (2008)
Moghadam, J., Roy Choudhury, R., Yin, H., Fox, A.: AutoStyle: toward coding style feedback at scale. In: 2nd ACM Conference on Learning at Scale, Vancouver (2015)
Nguyen, A., Piech, C., Huang, J., Guibas, L.: Codewebs: scalable code search for MOOCs. In: 23rd International Conference on world wide web, Seoul (2014)
Rivers, K., Koedinger, K.R.: Data-driven hint generation in vast solution spaces: a self-improving python programming tutor. Int. J. Artif. Intell. Educ., 1–28 (2015). http://dx.doi.org/10.1007/s40593-015-0070-z
Shute, V.J.: Focus on formative feedback. Rev. Educ. Res. 78(1), 153–189 (2008)
Yin, H., Moghadam, J., Fox, A.: Clustering student programming assignments to multiply instructor leverage. In: 2nd ACM Conference on Learning at Scale, Vancouver (2015)
Zhang, K., Shasha, D.: Simple fast algorithms for the editing distance between trees and related problems. SIAM J. Comput. 18(6), 1245–1262 (1989)
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Roy Choudhury, R., Yin, H., Fox, A. (2016). Scale-Driven Automatic Hint Generation for Coding Style. In: Micarelli, A., Stamper, J., Panourgia, K. (eds) Intelligent Tutoring Systems. ITS 2016. Lecture Notes in Computer Science(), vol 9684. Springer, Cham. https://doi.org/10.1007/978-3-319-39583-8_12
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DOI: https://doi.org/10.1007/978-3-319-39583-8_12
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